Service-Oriented Resource Allocation in UAV-Assist Internet of Things
Internet of Things (IoT) is a promising technology for realizing massive interconnection, especially when it is enabled by Unmanned Aerial Vehicles (UAVs) in terms of flexibility and operability. However, limited cache resources and channel resources limit the implementation of high throughput and l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10495032/ |
| _version_ | 1827259944549744640 |
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| author | Li Wang Jin Song Weijie Yu Yuqi Teng |
| author_facet | Li Wang Jin Song Weijie Yu Yuqi Teng |
| author_sort | Li Wang |
| collection | DOAJ |
| description | Internet of Things (IoT) is a promising technology for realizing massive interconnection, especially when it is enabled by Unmanned Aerial Vehicles (UAVs) in terms of flexibility and operability. However, limited cache resources and channel resources limit the implementation of high throughput and low-loss communication systems. Furthermore, in order to better user experience and system resource utilization, it is imperative for different services to acquire individualized resource configurations. This paper proposes a Service-Oriented Resource Allocation (SoRA) algorithm based on a scenario involving multiple users, a relay, and sink. Firstly, SoRA uses a fuzzy complementary matrix to evaluate different types of service. Secondly, the storage and communication resources are allocated jointly to maximize system utility. Finally, the deep Q-learning strategy is adopted to allocate the resources dynamically according to the network conditions. The simulation results show that the proposed SoRA algorithm reduces the delay by 43.49%, reduces the average packet loss by 14.28%, and decreases the average power consumption by 25.36%. |
| first_indexed | 2024-04-24T05:41:15Z |
| format | Article |
| id | doaj.art-2790a676dd304f13b311e001233b5a79 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-03-22T02:34:38Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2790a676dd304f13b311e001233b5a792024-05-03T23:01:18ZengIEEEIEEE Access2169-35362024-01-0112545255453410.1109/ACCESS.2024.338676710495032Service-Oriented Resource Allocation in UAV-Assist Internet of ThingsLi Wang0https://orcid.org/0000-0001-7567-0306Jin Song1https://orcid.org/0009-0006-3737-5676Weijie Yu2Yuqi Teng3School of Software, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaSchool of Software, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaSchool of Software, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaSchool of Software, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaInternet of Things (IoT) is a promising technology for realizing massive interconnection, especially when it is enabled by Unmanned Aerial Vehicles (UAVs) in terms of flexibility and operability. However, limited cache resources and channel resources limit the implementation of high throughput and low-loss communication systems. Furthermore, in order to better user experience and system resource utilization, it is imperative for different services to acquire individualized resource configurations. This paper proposes a Service-Oriented Resource Allocation (SoRA) algorithm based on a scenario involving multiple users, a relay, and sink. Firstly, SoRA uses a fuzzy complementary matrix to evaluate different types of service. Secondly, the storage and communication resources are allocated jointly to maximize system utility. Finally, the deep Q-learning strategy is adopted to allocate the resources dynamically according to the network conditions. The simulation results show that the proposed SoRA algorithm reduces the delay by 43.49%, reduces the average packet loss by 14.28%, and decreases the average power consumption by 25.36%.https://ieeexplore.ieee.org/document/10495032/Unmanned aerial vehicleInternet of Thingsresource allocationdeep reinforcement learning |
| spellingShingle | Li Wang Jin Song Weijie Yu Yuqi Teng Service-Oriented Resource Allocation in UAV-Assist Internet of Things IEEE Access Unmanned aerial vehicle Internet of Things resource allocation deep reinforcement learning |
| title | Service-Oriented Resource Allocation in UAV-Assist Internet of Things |
| title_full | Service-Oriented Resource Allocation in UAV-Assist Internet of Things |
| title_fullStr | Service-Oriented Resource Allocation in UAV-Assist Internet of Things |
| title_full_unstemmed | Service-Oriented Resource Allocation in UAV-Assist Internet of Things |
| title_short | Service-Oriented Resource Allocation in UAV-Assist Internet of Things |
| title_sort | service oriented resource allocation in uav assist internet of things |
| topic | Unmanned aerial vehicle Internet of Things resource allocation deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10495032/ |
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